2025/09/29 by Javier Aguilar, Anonymous, Miguel A. Muñoz +4
Arts and Humanities · Biochemistry, Genetics and Molecular Biology · Computer Science · Environmental Science · Neuroscience · Physics and Astronomy · #Ecosystem dynamics and resilience #Embodied and Extended Cognition #Estimator #Gaussian Processes and Bayesian Inference #Gene Regulatory Network Analysis #Importance sampling #Inference #Limit (mathematics) #Model selection #Monte Carlo method #Philosophy and History of Science #Population #Sampling (signal processing) #Statistical inference #Stochastic approximation #Stochastic modelling #Stochastic process #stochastic dynamics and bifurcation
paper · pdf · open access · doi:10.1103/tmkr-9kl2
published in Physical Review X 16(3) (American Physical Society)
openalex publication_date 2026/05/21 · openalex created_date 2026/05/22 · openalex updated_date 2026/07/24
Robust inference for stochastic dynamical systems is often hampered by sparse sampling and the absence of closed-form likelihoods. We introduce a Monte Carlo path-inference framework that leverages full-path statistics and bridge processes to deliver reliable parameter estimation and model selection from coarsely sampled time series, without requiring analytical solutions. Crucially, we couple mechanistic stochastic models with their inference procedures to quantify how experimental design—specifically, sampling frequency and dataset size—governs estimator precision and model distinguishability. This analysis reveals optimal sampling regimes and sharp, resolution-dependent limits beyond which competing models become empirically indistinguishable. We validate the approach across four disparate systems—trajectories of optically trapped particles, human microbiome dynamics, social-media topic mentions, and forest population time series—recovering parameters and identifying when inference is fundamentally constrained by measurement resolution, thereby clarifying ongoing debates about dominant noise sources in these systems. Together, these results establish path-based Monte Carlo as a practical, general tool for inference and model discrimination in complex systems and provide principled guidelines for designing measurements that maximize information under real-world constraints.